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1
A Hybrid Intrusion Detection System for SDWSN using Random Forest (RF) Machine Learning Approach
A Hybrid Intrusion Detection System for SDWSN using Random Forest (RF) Machine Learning Approach
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A Hybrid Intrusion Detection System for SDWSN using Random Forest (RF) Machine Learning Approach

International journal of advanced computer science & applications, 2020, Vol.11 (2)

2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2158-107X ;EISSN: 2156-5570 ;DOI: 10.14569/IJACSA.2020.0110236

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2
Exploring best-matched embedding model and classifier for charging-pile fault diagnosis
Exploring best-matched embedding model and classifier for charging-pile fault diagnosis
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Exploring best-matched embedding model and classifier for charging-pile fault diagnosis

Cybersecurity (Singapore), 2023-12, Vol.6 (1), p.7-13, Article 7 [Peer Reviewed Journal]

The Author(s) 2023 ;The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2523-3246 ;EISSN: 2523-3246 ;DOI: 10.1186/s42400-023-00138-z

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3
Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning Methods
Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning Methods
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Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning Methods

Advances in Electrical and Computer Engineering, 2014-02, Vol.14 (1), p.121-132 [Peer Reviewed Journal]

COPYRIGHT 2014 Stefan cel Mare University of Suceava ;ISSN: 1582-7445 ;EISSN: 1844-7600 ;DOI: 10.4316/AECE.2014.01019

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4
Detection of Malicious Websites Using Symbolic Classifier
Detection of Malicious Websites Using Symbolic Classifier
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Detection of Malicious Websites Using Symbolic Classifier

Future internet, 2022-12, Vol.14 (12), p.358 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1999-5903 ;EISSN: 1999-5903 ;DOI: 10.3390/fi14120358

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5
Fast Support Vector Machine Classification for Large Data Sets
Fast Support Vector Machine Classification for Large Data Sets
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Fast Support Vector Machine Classification for Large Data Sets

International journal of computational intelligence systems, 2014-01, Vol.7 (2), p.197-212 [Peer Reviewed Journal]

the authors 2014 ;ISSN: 1875-6891 ;ISSN: 1875-6883 ;EISSN: 1875-6883 ;DOI: 10.1080/18756891.2013.868148

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6
SVM prediction of the attestation success on the base of the poll results
SVM prediction of the attestation success on the base of the poll results
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SVM prediction of the attestation success on the base of the poll results

ITM Web of Conferences, 2018, Vol.18, p.4002 [Peer Reviewed Journal]

2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2271-2097 ;ISSN: 2431-7578 ;EISSN: 2271-2097 ;DOI: 10.1051/itmconf/20181804002

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7
Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition
Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition
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Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition

Soft computing (Berlin, Germany), 2013, Vol.17 (1), p.1-16 [Peer Reviewed Journal]

Springer-Verlag 2012 ;Springer-Verlag 2012. ;ISSN: 1432-7643 ;EISSN: 1433-7479 ;DOI: 10.1007/s00500-012-0885-6

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8
Diversity in Classifier Ensembles
Diversity in Classifier Ensembles
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Book Chapter 		Add to My Research

Diversity in Classifier Ensembles

Combining Pattern Classifiers, 2014, p.247-289

Copyright © 2014 John Wiley & Sons, Inc. All rights reserved. ;ISBN: 1118315235 ;ISBN: 9781118315231 ;EISBN: 1118914554 ;EISBN: 9781118914557 ;EISBN: 9781118914540 ;EISBN: 1118914546 ;EISBN: 1118914562 ;EISBN: 9781118914564 ;DOI: 10.1002/9781118914564.ch8 ;OCLC: 878051089 ;LCCallNum: TK7882.P3 -- .K863 2014eb

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